Image feature extraction using diameter-limited gradient direction histograms

Features extracted by operators which examine diameter-limited gradient direction histograms are important because they describe images of industrial workpieces efficiently and have the potential for rapid computation via special purpose hardware. When such operators are passed over an image in raster fashion, features such as the following are detected: a strong peak in the direction histogram indicating the presence of a relatively straight edge, a second strong direction indicating a corner, a wide direction group indicating a curved edge, and a uniform distribution of directions over the histogram indicating small holes. Diameter-limited optimization can be used to substantially reduce the number of pixels which have been given feature labels by such operators without losing descriptive power. Feature labels for direction histograms having a second strong direction, a wide direction group, or a uniform distribution might be retained only if all other pixels within a circular aperture have a lower bin value. Pixels with a strong direction label might be retained only if all other pixels within a circular aperture and along the gradient direction have a smaller bin value. Tracking can then be applied to the remaining strong direction pixels in the direction perpendicular to the gradient to achieve representation of edges by endpoints. Experimental results indicate that descriptions which are compatible with human interpretation can be achieved.